Multidimensional frailty in older people in general practitioners' clinical practice: the SELFY-MPI SIGOT project

The multidimensional prognostic index (MPI) is a comprehensive geriatric assessment (CGA) tool exploring the multiple domains of older subject. The knowledge and the diffusion of self-assessment tools for identifying frailty in general medicine is still limited. The aim of our study is to determine the prevalence of frailty using a multidimensional frailty screening tool (SELFY-MPI) in a cohort of older adults, belonging to the general practitioner's (GPs) experience. In the frame of a national educational program organized by the Italian Geriatric Hospital and Community Society (SIGOT), expert geriatricians carried-out local courses addressed to GPs, focused on multidimensional approach in primary care. A cross-sectional study of the SELFY-MPI, based on eight different domains, in the general practitioners' outpatient clinic was performed among 50 GPs. SELFY-MPI risk score was used for dividing the participants in robust, pre-frail, or frail. A total of 526 participants (mean age: 77.7 years; females=55.3%) fulfilled the SELFY-MPI. The participants were, on average, independent in the activities of daily living, had a good mobility, but they reported some cognitive difficulties, and they can be considered at risk of malnutrition. A high prevalence of comorbidities and polypharmacotherapy was also present. The 20.2% of the sample lived alone, suggesting a potential social frailty. The mean SELFY-MPI score was 0.26 0.17: therefore, 21.67% of the participants were categorized as pre-frail, and 3.99% as frail. Pre-frailty and frailty are common in GPs experience. SELFY-MPI is a feasible screening tool for multidimensional frailty in the GPs clinical practice

Non-selective non-steroidal anti-inflammatory drugs (NSAIDs) and cardiovascular risk

NSAIDs are largely used for the treatment of a huge variety of clinical conditions in order to relieve symptoms related to inflammation.The use of NSAIDs is associated with a potential increased risk of gastrointestinal and cardiovascular complications.The cardiovascular risk related to NSAIDs administration is often underestimated and it is frequently believed to be less important than the gastrointestinal risk. Adverse effects of NSAIDs are specifically related to their underlying mechanisms of action.The most plausible mechanism underlying the cardiovascular risk of NSAIDs has been identified in the profound inhibition of COX-2-dependent PGI2 in the presence of incomplete and intermittent inhibition of platelet COX-1. Nevertheless, the cardiovascular risk related to the use of NSAIDs is not only due to the COX-2 selectivity. An important determinant of the clinical effects of NSAIDs depends on the pharmacokinetic features of the different drugs such as half-life, and type of formulations, which can influence the extent and duration of patient exposure to COXisozyme inhibition. The aim of this review is to analyse the mechanisms behind the cardiovascular risk of different NSAIDs

The "ARIANNA" Project: An Observational Study on a Model of Early Identification of Patients with Palliative Care Needs through the Integration between Primary Care and Italian Home Palliative Care Units

Objective: The aim of this study was to illustrate the characteristics of patients with palliative care (PC) needs, early identified by general practitioners (GPS), and to analyze their care process in home PC services. Background: Early identification and service integration are key components to providing quality palliative care (PC) services ensuring the best possible service for patients and their families. However, in Italy, PC is often provided only in the last phase of life and for oncological patients, with a fragmented service. Methods: Multicenter prospective observational study, lasting in total 18 months, implemented in a sample of Italian Home Palliative Care Units (HPCUs), enrolling and monitoring patients with limited life expectancy, early identified by 94 GPS. The study began on March 1, 2014 and ended on August 31, 2015. Results: Nine hundred thirty-seven patients, out of a total pool of 139,071, were identified by GPS as having a low life expectancy and PC needs. Of these, 556 (59.3%) were nononcological patients. The GPS sent 433 patients to the HPCUs for multidimensional assessment, and 328 (75.8%) were placed in the care of both settings (basic or specialist). For all patients included in the study, both oncological and nononcological patients, there was a high rate of death at home, around 70%. Discussion: This study highlights how a model based on early identification, multidimensional evaluation, and integration of services can promote adequate PC, also for noncancer patients, with a population-based approach

How to Treat COVID-19 Patients at Home in the Italian Context: An Expert Opinion

The impact of the coronavirus disease (COVID-19), caused by the novel coronavirus SARS-CoV-2, continues to be widespread, with more than 100 million cases diagnosed in more than 220 countries since the virus was first identified in January 2020. Although patients with mild to moderate forms of COVID-19 could be efficiently managed at home, thus reducing the pressure on the healthcare system and minimizing socio-psychological impact on patients, no trial has been proposed, conducted, or even published on COVID-19 home therapy to date. These expert opinions provide indications on the therapeutical at home management of COVID-19 patients, based on the evidence from the literature and on current guidelines

Frailty detection among primary care older patients through the Primary Care Frailty Index (PC-FI)

: The prompt identification of frailty in primary care is the first step to offer personalized care to older individuals. We aimed to detect and quantify frailty among primary care older patients, by developing and validating a primary care frailty index (PC-FI) based on routinely collected health records and providing sex-specific frailty charts. The PC-FI was developed using data from 308,280 primary care patients ≥ 60 years old part of the Health Search Database (HSD) in Italy (baseline 2013-2019) and validated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K; baseline 2001-2004), a well-characterized population-based cohort including 3363 individuals ≥ 60 years old. Potential health deficits part of the PC-FI were identified through ICD-9, ATC, and exemption codes and selected through an optimization algorithm (i.e., genetic algorithm), using all-cause mortality as the main outcome for the PC-FI development. The PC-FI association at 1, 3 and 5 years, and discriminative ability for mortality and hospitalization were tested in Cox models. The convergent validity with frailty-related measures was verified in SNAC-K. The following cut-offs were used to define absent, mild, moderate and severe frailty: < 0.07, 0.07-0.14, 0.14-0.21, and ≥ 0.21. Mean age of HSD and SNAC-K participants was 71.0 years (55.4% females). The PC-FI included 25 health deficits and showed an independent association with mortality (hazard ratio range 2.03-2.27; p < 0.05) and hospitalization (hazard ratio range 1.25-1.64; p < 0.05) and a fair-to-good discriminative ability (c-statistics range 0.74-0.84 for mortality and 0.59-0.69 for hospitalization). In HSD 34.2%, 10.9% and 3.8% were deemed mildly, moderately, and severely frail, respectively. In the SNAC-K cohort, the associations between PC-FI and mortality and hospitalization were stronger than in the HSD and PC-FI scores were associated with physical frailty (odds ratio 4.25 for each 0.1 increase; p < 0.05; area under the curve 0.84), poor physical performance, disability, injurious falls, and dementia. Almost 15% of primary care patients ≥ 60 years old are affected by moderate or severe frailty in Italy. We propose a reliable, automated, and easily implementable frailty index that can be used to screen the primary care population for frailty

Characteristics of non-sudden deaths in Belgium, the Netherlands, Italy and Spain; % (95% CI), n.

<p>CI = confidence interval.</p><p>Percentages are within-country percentages. Percentages are rounded and thus may not add up to 100.</p><p>Nursing home deaths from the Netherlands (n = 52) were excluded.</p><p>Missing values: age n = 12 (0.3%), sex n = 12 (0.3%), place of death n = 15 (0.3%), cause of death n = 53 (1.2%).</p><p>Pearson <i>χ</i>² test.</p><p>Not included in significance tests.</p

Use of palliative care provided GPs and use of and number of days in specialist palliative care in the last three months of life; % (95% CI), n.

<p>IQR = inter-quartile range; CI = confidence interval.</p><p>Percentages are within-country percentages. Percentages are rounded and thus may not add up to 100.</p><p>Missing values: SPC n = 191 (4.3%); time of initiation of SPC n = 174 (3.9% of those who received SPC); palliative care by GP n = 55 (1.2%).</p><p>Palliative care categories are not mutually exclusive.</p><p>p-values based on multivariate analyses adjusted for age, sex, cause and place of death.</p><p>Kruskal-Wallis test (bivariate analysis).</p

Factors associated with use of palliative care provided by GPs and specialist palliative care services^*.

<p>OR = odds ratio; CI = confidence interval; Ref = reference category.</p><p>All percentages indicate proportions within the independent variable. Percentages are rounded and thus may not add up to 100.</p><p>Missing values for dependent variables: specialist palliative care n = 191 (4.3%); GP palliative care n = 55 (1.2%); missing values for independent variables: age n = 12 (0.3%), sex n = 12 (0.3%), cause of death n = 53 (1.2%), place of death n = 15 (0.3%).</p><p>Odds ratios in bold indicate statistically significant associations.</p><p>Independent variables age and cause of death were correlated (r = .40, p<.01). Variance inflation factors did not indicate problems of multicollinearity.</p><p>Two multivariate logistic regression analyses with 1) palliative care by the GP and 2) specialist palliative care as dependent variable.</p><p>Specialist palliative care and palliative care by the GP are not mutually exclusive categories.</p><p>Not included in significance tests.</p><p>OR not meaningful as 100% of cases have the same value on the dependent variable.</p><p>Missing values on the independent variables resulted in missing cases in the multivariate logistic regression analyses. The number of deaths included in the analyses are indicated.</p